yuweihao/MambaOut

Health v2 · maintenance only

Adoption not part of the score

2704 stars · 49 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MambaOut is a PyTorch implementation of Gated CNN models from the CVPR 2025 paper 'MambaOut: Do We Really Need Mamba for Vision?', which questions whether state space models (Mamba) are necessary for vision tasks. It provides model definitions and pretrained ImageNet checkpoints showing Gated CNNs outperform visual Mamba models on image classification.

Use cases

• train image classification models without mamba
• compare gated cnn vs vision mamba performance
• get pretrained imagenet checkpoints for gated cnn
• reproduce cvpr 2025 mambaout paper results
• use mambaout models in timm for transfer learning
• study whether ssm is needed for vision backbones

When to choose

• you need a strong convolutional vision backbone without state space models
• you want to benchmark Mamba vs Gated CNN architectures on ImageNet
• you want lightweight pretrained models competitive with ViT-S

When to avoid

• you specifically need sequence modeling or state space models for vision
• you need detection or segmentation backbones, which the paper does not cover
• you want a production-ready inference service rather than research code

Facets

library · maturity stable

machine-learning deep-learning image-processing computer-vision deep-learning machine-learning python pytorch image-classification gated-cnn state-space-models research-code cvpr-2025 pretrained-models gpu

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